Near-infrared spectroscopic analysis of blood vessel walls

ABSTRACT

The invention relates to methods and devices for characterizing tissue in vivo, e.g., in walls of blood vessels, to determine whether the tissue is healthy or diseased, and include methods of displaying results with or without thresholds.

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is a continuation-in-part application of, andclaims priority from, U.S. patent application Ser. No. 10/212,845, filedon Aug. 5, 2002, and claims the benefit of priority of U.S. ProvisionalApplication No. 60/401,394, filed on Aug. 5, 2002. The contents of bothapplications are incorporated herein by reference in their entirety.

TECHNICAL FIELD

[0002] This invention relates to near-infrared spectroscopic examinationof blood vessels to detect and characterize tissue, e.g., lesions suchas atherosclerotic plaques.

BACKGROUND

[0003] Atherosclerosis is an arterial disorder involving the intima ofmedium- or large-sized arteries, including the aortic, carotid,coronary, and cerebral arteries. Atherosclerotic lesions or plaquescontain a complex tissue matrix, including collagen, elastin,proteoglycans, and extracellular and intracellular lipids with foamymacrophages and smooth muscle cells. In addition, inflammatory cellularcomponents (e.g., T lymphocytes, macrophages, and some basophils) canalso be found in these plaques. Disruption or rupture of atheroscleroticplaques appears to be the major cause of heart attacks and strokes,because after the plaques rupture, local obstructive thromboses formwithin the blood vessels. Although the risk of plaque rupture usuallycannot be predicted, many postmortem examinations have revealed thatthis risk depends mainly on plaque composition. Most rupturedatherosclerotic plaques are characterized structurally by the formationof a large, soft, lipid-rich, necrotic core covered by a thin fibrouscap, densely infiltrated by macrophages. Of these features, lipidaccumulation in so-called “lipid pools” is the most frequently observedprecondition for rupture. Inflammation is also a major feature ofnonruptured, but eroded, thrombosed plaques.

[0004] Near infrared (NIR) spectroscopy has been used in industry forover 20 years for analysis of chemical materials either quantitativelyor qualitatively. It has played a significant role in process andproduct control functions, because the spectra are not severely affectedby atmospheric water or carbon dioxide. NIR spectra consist of overtonesand combinations of fundamental IR bands, which can lower the resolutionof the spectral features compared to other spectroscopic methods thathave narrower bands, such as infrared (IR). However, new statisticaltechniques can be used to extract useful information from the lowerresolution NIR spectral data. For example, chemometrics, which combinesspectroscopy and mathematics, can provide clear qualitative as well asquantitative information. Thus, NIR spectroscopy combined withchemometrics has been used more frequently in a number of disciplines.

[0005] For example, NIR spectra have been obtained of biological tissuesamples in vitro. In addition, some efforts have been made to imagetissues in vivo; however, such imaging poses numerous challenges,including the problem of imaging though whole blood, which can mask andobscure spectral images of desired targets.

SUMMARY

[0006] The invention is based, in part, on the discovery that if oneuses two or more single wavelengths and/or one or more narrow wavelengthbands (covering, e.g., 2 to 10, 20, 30, or more nanometers up to about100 nm) within a specific range of NIR wavelengths (1100 to 1415nanometers), one can characterize vascular tissue, in vivo, e.g.,through whole blood, to determine the composition of the tissue, e.g.,the chemical composition, including the presence of lipid components,which can indicate whether a particular tissue is diseased or notdiseased. In particular, one can characterize tissue as having or nothaving a lesion, e.g., a lesion that is “vulnerable,” i.e., likely torupture, and thus life-threatening, or “safe,” and thus notlife-threatening. Thus, the invention features methods of discriminatingbetween diseased and healthy tissue in a blood vessel wall in vivo. Theinvention also features methods of analyzing the results of the newmethods with or without the use of specific thresholds against which thetissue characteristics can be compared.

[0007] In general, the invention features methods of spectroscopicallyanalyzing blood vessel walls in vivo using two or more singlewavelengths or any one or more wavelength bands, each covering 1 to 10,20, 30, or more nanometers of NIR radiation within a wavelength range of1100 to 1415 nm, to illuminate the blood vessel walls. The use of thesesingle wavelengths or narrow bands within this small wavelength rangesimplifies data acquisition and analysis, yet provides highly accurateand repeatable results. The vessel walls can be illuminated and thediffusely reflected light resulting from illumination of the walls canbe analyzed either with blood in the vessel, or optionally with bloodremoved or replaced, e.g., temporarily, from the vessel.

[0008] Due to the nature of devices that generate light and radiation,illumination of a sample or target tissue with a “single wavelength”means illumination with a peak intensity at a specific wavelength, alongwith some illumination of the adjacent wavelengths.

[0009] The methods include illuminating the vessel wall and thencollecting the radiation reflected via an optical fiber within acatheter then converting the reflectance intensities into absorbanceintensities as a function of wavelength, and optionally pre-processingthe spectra using techniques, such as mean centering (MC), autoscaling,normalization, first and second derivatives, smoothing options such asSavitzky Golay smoothing, varied baseline removal techniques, orthogonalsignal correction, generalized least squares filtering, wavelets,standard normal variant (SNV) techniques, multiplicative scattercorrection (MSC) techniques, and other techniques used to removeunwanted signals.

[0010] In another aspect, the invention features an in vivo method forcharacterizing tissue in a blood vessel wall by (a) illuminating atissue in the blood vessel wall with any two or more single wavelengthsor one or more narrow wavelength bands of near-infrared radiation withina wavelength range of about 1100 to 1415 nm; (b) detecting radiationreflected from the tissue having a wavelength of from about 1100 to 1415nm; (c) processing the detected radiation to characterize the tissue inthe blood vessel wall; and (d) providing an output indicating the tissuecharacterization.

[0011] The invention also includes a method of analyzing tissue in bloodvessel walls in vivo utilizing a fiber optic probe by introducing theprobe into a blood vessel; directing onto the tissue in the blood vesselwall near-infrared radiation comprising any two or more singlewavelengths or one or more narrow wavelength bands within a wavelengthrange of about 1100 to 1415 nm; detecting radiation within a wavelengthrange from substantially 1100 to 1415 nm not absorbed by the bloodvessel wall; and analyzing the detected radiation to categorize thetissue in the blood vessel wall.

[0012] In all of these methods, the one or more narrow wavelength bandscan each span about 1.0 nm to about 100 nm within the wavelength rangeof 1100 to 1415 nm. Alternatively, the method can use two singlewavelengths, or two narrow wavelength bands, each spanning 1.0 nm to 30nm within the wavelength range of 1100 to 1415 nm, or at least onenarrow wavelength band and at least one single wavelength.

[0013] In these methods, the wavelength range can be about 1100 to 1350nm, 1150 to 1250 nm, 1175 to 1280 nm, or about 1190 to 1250 nm. Theblood vessel can be an artery, e.g., a coronary artery, and the tissuecan include a lipid pool, a lipid pool and a thin fibrous cap, a lipidpool and a thick fibrous cap, and fibrotic and/or calcific tissue. Themethod can be used to illuminate tissue through blood, e.g., through 1,2, 3 or more mm of blood, and reflected radiation is detected throughthe blood. In some embodiments, the blood in the blood vessel can beoccluded, e.g., by a balloon or catheter. Alternatively, the bloodvessel can be filled with a biocompatible liquid, in which case theblood vessel wall is illuminated through the biocompatible liquid, andreflected radiation is detected through the biocompatible liquid.

[0014] In certain embodiments, the method can also include illuminatingthe tissue in the blood vessel wall with any two or more singlewavelengths or one or more narrow wavelength bands of near-infraredradiation within a second wavelength range of about 1650 nm to 1780 nm;and further detecting radiation reflected from the tissue having asecond wavelength of from about 1650 nm to 1780 nm. This secondwavelength range can also be about 1650 to 1730 nm.

[0015] In all of these methods, the processing can be done usingchemometric discrimination algorithms, and the methods can furtherinclude preprocessing the detected radiation to remove spectralinformation not related to a characterization of the tissue. Forexample, the methods can use qualitative chemometric discriminationalgorithms, such as partial least squares-discriminate analysis(PLS-DA), principle component analysis with Mahalanobis Distance(PCA-MD), or principle component analysis with Mahalanobis Distance andaugmented residuals (PCA/MDR). Alternatively, the methods can usequantitative chemometric algorithms, such as partial least squares (PLS)or principal component analysis (PCA).

[0016] In these methods, the output can provide a continuous grading ofthe scanned tissue, or can categorize the scanned tissue into two,three, or more different categories of lesions, or can categorize thescanned tissue as either healthy or a vulnerable plaque. The output canalso be a graphical representation of the signals corresponding to thereflectance spectra, or a color scheme of the tissue characterization.

[0017] In certain of the methods, the processing can include applying athreshold to determine whether the scanned tissue is diseased or not,e.g., applying a threshold determined by optimizing the separationbetween two or more groups to establish a boundary calculation thatdetermines whether the scanned tissue is diseased or not. In specificembodiments, the output can categorize the tissue as lipid-rich or not,as lipid-rich, calcific, fibrotic, normal, or other, as a thin-cappedfibroatheroma (TCFA) or not, or as a vulnerable lesion or not.Alternatively, the output can categorize the tissue as diseased or notwithout applying a threshold.

[0018] In another aspect, the invention also includes an apparatus forcharacterizing tissue in vivo that includes a near-infrared radiationsource that generates radiation comprising any two or more singlewavelengths or one or more narrow wavelength bands of near-infraredradiation within a wavelength range of about 1100 to 1415 nm, e.g., 1100to 1350 or 1150 to 1250 nm; one or more radiation conduits fortransmitting radiation from the radiation source to the tissue and forreceiving radiation not absorbed by the tissue; a radiation detectorthat collects radiation not absorbed by the tissue across a wavelengthrange of substantially 1100 to 1415 nm; a processor that processes thecollected radiation to characterize the tissue; and an output devicethat indicates the characterization of the tissue. For example, thenear-infrared radiation source can generate two narrow wavelength bands,each spanning 1.0 nm to 30 nm within the wavelength range of 1100 to1415 nm. The apparatus can further include a near-infrared radiationsource that generates radiation comprising any two or more singlewavelengths or one or more narrow wavelength bands of near-infraredradiation within a second wavelength range of about 1650 to 1780 nm. Thesource for the second wavelength range can be the same as or differentfrom the source that generates the first wavelength range.

[0019] In these devices, the output device can provide a graphicalrepresentation of the radiation diffusely reflected from the scannedtissue, a functional color scheme of the scanned tissue, or a continuousgrading of the scanned tissue. Alternatively, the processor and outputdevice can categorize the scanned tissue into two, three, or moredifferent categories of lesions, e.g., as either healthy or a vulnerableplaque. The processor can apply a threshold to determine whether thescanned tissue is diseased or not, e.g., a threshold determined byminimizing a classification between two or more groups to establish aboundary calculation that determines whether the scanned tissue isdiseased or not.

[0020] In other embodiments, the apparatus can include an output devicethat has a screen that shows basic patient information, the date andtime of a scan, a digitized longitudinal view of a scanned tissue, and adigitized cross-section of a particular section of scanned tissue. Incertain embodiments, the digitized longitudinal view and cross-sectionsof the scanned tissue are separated into sections, where each sectionindicates that the point of tissue represented by that section is eitherhealthy or diseased. Alternatively, each section can indicate one of acontinuous grade of a plurality of colors or shades of gray representingthe health of the tissue at that point. The processor and output devicecan also provide constituent concentrations of the scanned tissue.

[0021] In another embodiment, the invention includes an instrument forcharacterizing portions of tissue in vivo that includes a) means forilluminating portions of tissue with near-infrared radiation comprisingany two or more single wavelengths or one or more narrow wavelengthbands of near-infrared radiation within a wavelength range of about 1100to 1415 nm; b) means for collecting radiation within the wavelengthrange that is not absorbed by the tissue; c) means for determining fromthe collected radiation the amounts of absorbance of radiation by theilluminated tissue; and d) means for discriminating one illuminatedtissue component from another illuminated tissue component within thewavelength range, wherein the discriminating means that include i) meansfor preprocessing the absorbance amounts using a chemometricpreprocessing technique, and ii) means for performing a chemometricdiscrimination algorithm on the preprocessed absorbance amounts tocharacterize the tissues; and e) means for providing an outputindicating the characterization of the illuminated tissue.

[0022] In yet another embodiment, the invention also features methods ofanalyzing tissue in blood vessel walls in vivo utilizing a fiber opticprobe, by introducing the probe into a blood vessel; directing onto thetissue in the blood vessel wall near-infrared radiation comprising anytwo or more single wavelengths or one or more narrow wavelength bandswithin a wavelength range of about 1100 to 1415 nm; detecting radiation,within a wavelength range from substantially 1100 to 1415 nm, which isnot absorbed by the blood vessel wall; and analyzing the detectedradiation to categorize the tissue in the blood vessel wall.

[0023] The invention also features methods of displaying spectral datacorresponding to a tissue by (a) scanning a series of points within thetissue with radiation, e.g., near-infrared; (b) detecting radiationreflected from the tissue; (c) processing the detected radiation togenerate a set of numbers wherein each number in the set characterizes adifferent point of scanned tissue; and (d) converting the set of numbersinto a continuous grade output that characterizes the tissue without athreshold. These methods permit tissues to be characterized byconstituent concentrations within the scanned tissue.

[0024] All of the methods herein can further include applyingchemometric algorithms designed to characterize a lesion based on itstissue type or by the presence of specific chemical compositions. Thesealgorithms can be developed to operate independently of blood depth,using spectra acquired at various blood depths, or developed usingtissue type data and/or the presence of specific chemical compositionsas determined by standard methods (e.g., pathology or chemicalanalysis).

[0025] In certain embodiments, the lesions are designated as fallingwithin one of two categories: vulnerable (life-threatening) and safe(not life-threatening). One or more tissue types or specific chemicalcompositions may fall within each category. For example, a lesion withhigh lipid content and a thin fibrous cap is considered a “vulnerable”plaque, e.g., one which is life threatening. Lesions that mainly containfibrous or calcific tissue, and/or have a high lipid content, but with athick fibrous cap, as well as normal or pre-atheroma tissues, arecharacterized as “safe.”

[0026] In certain embodiments, the tissue or lesion is continuouslygraded by the output of the algorithm as to its “vulnerabilitypotential” where normal tissue or a stable, safe lesion may bearbitrarily designated a low risk index (e.g. <1.0) and a plaque that isof very high risk is assigned a high risk index (e.g., 10).Alternatively, the continuous grading can be represented by a falsecolor scale, e.g., red at one end of the continuous range, and thenprogressing through the colors of the rainbow to violet at the other endof the range. A gray scale or different tones, pitches, or volumes ofsound can also be used. In some embodiments, both a threshold and acontinuous grading scheme are used in the same method or system toprovide a more accurate and robust indication of the results.

[0027] In other embodiments, the lesions are designated as fallingwithin one of three categories: vulnerable (high lipid content, thincap), potentially vulnerable (i.e., monitor the lesion over time; highlipid, thick cap), and safe (fibrous, calcific, normal, orpre-atheroma). In other embodiments, the lesions are designated asfalling within one of five categories: 1) lipid-containing tissue with athin fibrous cap, 2) lipid-containing tissue with a thick fibrous cap,3) fibrous tissue, 4) largely calcific tissue, and 5) normal orpre-atheroma tissue. These different categories can be used to provideadditional diagnostic and prognostic information as compared to themethods in which only two or three categories are provided. In this andother embodiments, the chemometric algorithm can be based on thepresence of other chemical compositions relating the lipid pool and thincap, along with other markers, to an index of vulnerability bydeveloping algorithms using spectra acquired from various tissue typesor chemical compositions which are known by standard methods (e.g.,pathology or chemical analysis) as part of the discrimination method.

[0028] In one embodiment, a threshold is used to categorize a particulartissue as either a thin-capped fibroatheroma (TCFA) or not, i.e., thetissue is either normal or is a safe lesion.

[0029] In other embodiments, the chemometric algorithm is applied toclassify spectral measurements into one of two or more depth classes(e.g., 0 to 1.5 mm and 1.5 mm and above away from the tissue). In this,and other embodiments, the chemometric algorithm can be based on thedepth classification, both in the presence of or without blood, and canbe applied to discriminate tissue types or the presence of chemicalcompositions, or the algorithm can be developed using spectra acquiredat various depths, and using tissue types or chemical compositions whichare known by standard methods (e.g., pathology or chemical analysis) aspart of the discrimination method.

[0030] The methods can be further extended to include applyingchemometric algorithms designed to quantitatively characterize a lesionbased upon the presence of specific chemical compositions. Thesealgorithms can be used to predict the specific chemical compositions ofeach lesion as determined by standard methods (e.g., pathology orchemical analysis).

[0031] Unless otherwise defined, all technical and scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which this invention belongs. Although methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of the present invention, suitable methods andmaterials are described below. All publications, patent applications,patents, and other references mentioned herein are incorporated byreference in their entirety. In case of conflict, the presentspecification, including definitions, will control. In addition, thematerials, methods, and examples are illustrative only and not intendedto be limiting.

[0032] Other features and advantages of the invention will be apparentfrom the following detailed description, and from the claims.

DESCRIPTION OF DRAWINGS

[0033]FIG. 1 is a schematic diagram of an imaging system forspectroscopically analyzing blood vessel walls using the new methods.Either a single fiber may be used for both delivery and detection oflight or multiple illumination-detection fiber channels can be employed.

[0034]FIG. 2 is a plot of NIR absorbance versus wavelength for varioushuman aorta samples resulting from illumination of the tissue with NIRradiation within the wavelength range of 1100 to 1850 nm. Spectra forcalcific, fibrotic, lipid-containing, and normal tissues are shown.

[0035]FIG. 3 is a plot of NIR absorbance versus wavelength for varioushuman aorta samples resulting from illumination of the tissue with NIRradiation within the wavelength range of 1100 to 1350 nm. Spectra forcalcific, fibrotic, disrupted plaques, lipid-containing, and normaltissues are shown.

[0036]FIGS. 4A and 4B are plots of diseased tissue samples as a functionof distance from the top of the tissue to the base of the fiber opticprobe spanning eight different tissue-to-probe separations from 0.0 mmto 3.0 mm with blood intervening. FIG. 4B is a plot of the data shown in4A, only the data have been offset by the absorbance at 1125 nm for eachspectrum.

[0037]FIG. 5 is a plot of NIR absorbance versus wavelength for the samehuman aortic tissue samples as those displayed in FIG. 3, but afterprocessing with a standard normal variant (SNV) technique. As shown, thespectra for calcific, fibrotic, disrupted plaque, lipid-containingtissues, and normal tissues, tend to overlap after SNV processing whichremoved the scattering differences of the samples.

[0038]FIG. 6 is a plot of NIR absorbance versus wavelength for the samehuman aortic tissue after processing with a standard normal variant(SNV) technique as seen in FIG. 5, but the whole group of spectra wasfurther processed by the addition of a mean centering (MC) techniquewhich provides a global mean for all the spectra used to build themodel. These techniques when combined indicate where the areas ofgreatest influence are within the spectral information.

[0039]FIG. 7 is a schematic of a processing technique combiningprincipal component analysis (PCA), a spectral decomposition methodwhich breaks the original data into two matrices that contain thePrincipal Component (PC) vectors and the resultants scalars or Scores(S), coupled with the addition of the spectral residuals (R) to createan appended Scores matrix called Sr, and then applying the non-linearstatistics of the Mahalanobis Distance (MD) calculations to the appendedScores (Sr) resulting in a discrimination method termed PCA/MDR.

[0040]FIG. 8A is a “projection plot” representing the mean predictionperformance results from multiple calibration models using two separate30 nm band regions and preprocessed using SNV plus MC. Specific areas ofhigh Mean Performance occur in the lighter shaded areas and inparticular in the regions from 1100 nm to 1415 nm.

[0041]FIGS. 8B and 8C are two additional projection plots of the meanperformance using two separate 30 nm bands as in FIG. 8A, but tested ata higher resolution of 2 nm intervals and spanning the region from 1100nm to 1350 nm. The highest Mean Performance for FIG. 8B occurred in thelighter shaded areas and in particular in the region from 1150 nm to1250 nm, and for FIG. 8C, the region spanned from 1175 nm to 1280 nm.

[0042]FIG. 9 is a plot of a partial least squares-discriminate analysis(PLS-DA) model of PCA scores between 1100 and 1350 nm. A line is drawnto best separate the scores from the lipid pool samples (LP) from thoseof all other samples (in this case they were the calcific (CAL) and thefibrotic (FIB) sample scores).

[0043]FIG. 10 is a schematic diagram of the NIR spectroscopyexperimental setup.

[0044]FIG. 11 is a schematic diagram of the histology layout used forprocessing the tissue specimen. Nine segments were sliced by hand(labeled A1 through C3) with the segments subtended by the FOSS® probeillumination circle (center circle) of most importance (segments A3, B1,B2, B3, C1) in the analysis. The stars indicate the approximate centerof each of the segments where the slices for slides were taken to avoidedge effects from the gross cutting.

[0045]FIGS. 12A and 12B are hypothetical graphs of chemometricprediction values. In FIG. 12A, a specific threshold is set todistinguish vulnerable plaques from other tissue types. In FIG. 12B, nothreshold is set, but the same two peaks as shown in FIG. 12A are usedto establish a continuous scale of chemometric prediction scores.

[0046]FIGS. 13A and 13B are representations of computer screens showingchemometric data for a vulnerable plaque diagnostic system. FIG. 13Ashows a system in which a threshold is used and FIG. 13B shows a systemin which no threshold is used.

[0047]FIG. 14 is a series of graphs showing sensitivity and specificitycurves for three different patients. If the same threshold is used forall three patients, some may receive an incorrect diagnosis.

[0048]FIGS. 15A to 15D are four different graphs of chemometricprediction scores. FIG. 15A is a standard graph showing two curves fordistributions of values for lipid-rich atheromas and all other tissuetypes. A threshold between sensitivity and specificity is set at thepoint where the two curves cross (as in FIG. 12A). FIG. 15B shows agraph of probabilities of particular values falling within either thelipid-rich atheroma or other tissue type categories. FIG. 15C is a graphsimilar to FIG. 15B, but shows a straight-line approximation of thecurve in FIG. 15B. FIG. 15D is a graph that does not show a probability,but a straight line (from 100 to 0 percent) in which every chemometricscore is equally important.

[0049]FIG. 16 is a representation of NIPALS decomposition of spectralinformation represented by matrix X (spectral data) and matrix Y(concentration data).

DETAILED DESCRIPTION General Methodology

[0050] In general, the invention features methods of using any two ormore single wavelengths or one or more narrow wavelength bands, eachcovering 1 to 10, 20, 30, 40, 50, 60, even 100 or more nm, of NIRradiation within the wavelength range of 1100 to 1415 nm (e.g., 1100 to1350 nm, 1100 to 1300 nm, 1150 to 1255 nm, 1200 to 1250 nm, or 1175 to1225 nm) to illuminate blood vessel walls in vivo, with or without thepresence of blood or a biocompatible liquid, such as a blood substitute,saline, or contrast medium, such as an iodine containing liquid (such asOnmipaque™ (iohexol)). Data obtained within these wavelengths enable theoperator to distinguish between diseased and healthy tissue locatedwithin a blood vessel wall.

[0051] The single wavelengths can be used in pairs or in combinations ofmultiple single discontinuous wavelengths, or in conjunction with one ormore of the narrow wavelength bands. The wavelength bands can be usedindividually, in pairs, or other multiples and with the same ordifferent band widths. For example, one can use a 10, 20, or 30 nm band,e.g., from the 1190 to 1220 nm region, and combine it with a band fromthe 1220 to 1250 nm region, as the incident NIR radiation to illuminateblood vessel walls in vivo.

[0052] More specifically, by obtaining data at two or more singlewavelengths, e.g., at 1190 and 1250 nm, or from one or more narrowwavelength bands, or a combination of single wavelengths with narrowband regions (e.g., 1190 to 1250 nm, or 1145 to 1175 nm with 1250 to1280 nm or 1175 to 1205 nm with 1310 to 1340 nm) one can get sufficientinformation to make a clear diagnosis of any lesion, such as anatherosclerotic plaque, in a blood vessel wall as either vulnerable orsafe. Of course, larger regions, such as the full 1100 to 1415 nm range,1150 to 1350 or 1250 nm, 1175 to 1250 nm, 1100 to 1200 nm, 1200 to 1300nm, 1250 to 1350 nm, 1215 to 1285 nm, and the like also work, andprovide even more information, but at the cost of added computationalcomplexity.

[0053] The narrow bands or regions covering 2, 5, 10, 20, 30, 40, up to100 or more nm, can also be used in pairs or triplets (or more), as longas at least one band region is within the overall range of 1100 to 1415nm. Thus, for example, illumination in the 1100 to 1415 nm wavelengthrange can be combined with illumination in the about 1560 to 1780 nmrange (e.g., 1600 to 1780, 1600 to 1700 nm, or 1650 to 1745 or 1730 nm).

[0054] If more than one band is used, the pairs or triplets can be, butneed not be, contiguous. For example, one can use a 30 nm band from 1190to 1220 nm, and a second band from 1220 to 1250 nm as the incident NIRradiation to illuminate blood vessel walls in vivo. Alternatively, onecan use a band that covers from 1145 to 1175 nm and another band thatcovers from 1250 to 1280 nm. One can also use a narrow band that coversfrom 1175 to 1205 nm and a band that covers from 1310 to 1340 nm. Otherpairs, triplets, or multiplets of bands or single intensities can beused. In addition, the pairs, triplets, or multiplets of bands can be ofdifferent sizes, e.g., a pair of bands where one covers a band of 20 nmand the other covers 40 nm, or the same size, e.g., a pair in which bothbands cover 30 nm. Other examples and combinations are possible. Forexample, one can use a single wavelength and a narrow band, or twosingle wavelengths, or two single wavelengths and two different bands.One can also use three, four, or more single wavelengths within the 1100to 1350 range.

[0055] One important aspect of the 1100 to 1350 range of wavelengths isthat it allows one to obtain relevant spectral data from blood vesselwalls in vivo through blood (and independent of the distance from theillumination tip to the vessel wall), which can otherwise interfere withaccurate readings at other wavelengths. This spectral data enablesaccurate characterization of the vessel wall in vivo as diseased or notdiseased tissue. Not only does this wavelength range enable collectionof data relevant to detecting lesions, such as atherosclerotic plaques,through blood within a living subject, such as a human or animal, itpermits the operator to characterize the lesion, i.e., to determinewhether a detected plaque is “vulnerable,” i.e., likely to rupture, or“safe,” i.e., unlikely to rupture. More specific characterization isalso possible.

[0056] The spectra received from the blood vessel walls are analyzed bytaking single point readings and determining whether the location of thevessel wall corresponding to that point reading is predominantly lipidwith a thin cap (vulnerable or “life-threatening”), lipid with a thickfibrous cap (potentially vulnerable), or predominantly non-lipid,normal, fibrotic, or calcific (safe or “non-life threatening”). Thus,the operator can create two (vulnerable/diseased or safe/healthy), three(vulnerable (diseased), potentially vulnerable (diseased), or safe(healthy)), or more different categories for lesion types as describedfurther herein. Alternatively, the system can provide a continuouslygraded output for the operator to decide whether a particular tissue isnormal or has a lesion that is vulnerable or safe, without the use of athreshold.

[0057] The new methods of analyzing tissue in vivo broadly include thesteps of directing NIR radiation onto the tissue to be analyzed (thoughblood or without blood) and then detecting the resultant radiation,converting the reflected signal to absorption values, optionallypreprocessing the returned signal, and processing the NIR radiationreflected by the tissue. The tissue lesions can be located in any bloodvessel, including the aorta and arteries such as the coronary, carotid,femoral, renal, and iliac arteries. As indicated above, the incident NIRradiation directed onto the blood vessel walls is any two or more singlewavelengths or any one or more narrow bands within the wavelength rangefrom 1100 to 1415 nm. To obtain the spectrum, the incident NIR radiationmust be directed onto the blood vessels walls, and the spectrum must becollected from the blood vessel walls. These steps are carried out usinga fiber optic probe or catheter that is introduced into the blood vesselin a subject, such as a human or animal, e.g., a domestic animal such asmammals, e.g., dogs, cats, horses, pigs, cows, rabbits, mice, hamsters,government officials, and the like. The new methods can also be carriedout on non-human primates, such as monkeys, but are typically used forhuman patients.

[0058] The spectrum can be collected using the same fiber optic cable oranother channel separate from the illumination fiber optic cable, whichcan be in the form of a catheter or probe that delivers the reflectanceillumination from a single light source as seen in FIG. 1. The detectorconverts the collected NIR radiation into an electrical signal, whichcan be subsequently processed using signal processing techniques.Alternatively, multiple illuminators and detectors can be used withinthe fiber optic system, using a single or multiple fiber assembly forboth illumination and collection of the NIR radiation.

[0059] The methods further include the steps of analyzing the electricalsignal corresponding to the reflectance spectra, and producing graphicalor other representations thereof. The electrical signal may be convertedto digital data. Advantageously, all of the steps provide high-speeddata acquisition and analysis, because only single wavelengths or narrowbands of wavelengths are used in the new methods. Such steps can beperformed using, for example, a processing chip, DSP, EPROM, etc., suchas those found in personal computers, and an appropriate algorithm,e.g., a chemometric algorithm, which can be embodied in the processingsoftware or processing hardware. Based on this analysis, vascular tissuecan be characterized as to its composition, or as to whether it isdiseased or not diseased. For example, the vessel wall may becharacterized as to whether it is high in lipid content, fibrouscontent, and/or calcific content. These parameters enable the user tocategorize the lesion as either vulnerable (diseased orlife-threatening) or safe (healthy or non-life-threatening). In otherembodiments, the lesions can be characterized as vulnerable(lipid-containing tissue with a thin fibrous cap 1) (diseased)),potentially vulnerable 2) (lipid-containing tissue with a thick fibrouscap (diseased)), 3) or safe (fibrous or calcific tissue, (but stilldiseased)). The “diseased” categories can be separated as well toprovide diseased categories or classifications, and a normal (healthy)category for a blood vessel wall without any lesions.

[0060] Alternatively, the methods can provide a continuously gradedoutput using, for example, a gray scale (in which white is a safe lesionand black is a vulnerable lesion, and varying levels of grey indicatevarious levels of vulnerability of the lesion), a false color scale(e.g., a red color could indicate a safe lesion and a violet color couldindicate a vulnerable lesion, with colors of the spectrum between themindicating various levels of vulnerability). In addition, varying sounds(such as tones, pitch, or volume) can also be used. For example, a slowseries of tones could indicate a safe lesion and a rapid series of tonescould indicate a vulnerable lesion. Tones of varying speeds in betweencould indicate various levels of vulnerability.

[0061] In addition, the methods can provide quantitative informationabout the constituents present within the lesion of interest. Also,lesions with higher levels of certain constituents, e.g., necrotic lipidpools or macrophages, can be used to indicate a level of vulnerability,as can other chemical components that are known to be responsible forplaque vulnerability.

[0062] Once a lesion or plaque is detected and determined to bevulnerable (or diseased), various technologies can be used for removingor stabilizing the plaque before it ruptures. For example, lasers can beused to ablate the plaque (see, e.g., Leon et al., J. Am. Coll. Radiol.,12:94-102, 1988; Gaffney et al., Lasers Surg. Med., 9:215-228, 1989).Alternatively, one can use brachytherapy, angioplasty, stenting (coatedor not), and photodynamic therapy.

Preprocessing of Data

[0063] The digital data may be first preprocessed to remove interferantsunrelated to the signal of interest, such as the baseline slope, theoffset, and/or the linear or multiplicative effects of scattering. Thespectral data can also be centered with respect to each other,autoscaled, normalized, etc. to enhance the small spectral featurespertaining to the component of interest

[0064] The benefits of preprocessing are demonstrated in an experimentin which spectra were obtained through bovine blood of four tissue typesfound in atheromatous human aortas. The absorbance versus wavelengthspectra are shown in FIG. 2 from 1100 nm to 1850 nm. Three diseasestates are represented as a composition of mainly lipid pool, fibrotic,or calcific, with one spectrum of the normal tissue. These diseasedstates were determined using the histology and pathology analysis andthen categorized and separated by the amount of each compositioncontained in that particular diseased plaque. FIG. 3 shows similarspectra within the smaller wavelength range of 1100 to 14150 nm. Fivecategories are shown, including calcific, fibrotic, disrupted plaque,lipid-containing, and normal. FIGS. 4A and 4B show spectra of a diseasedtissue sample viewed through various depths of sample to probeseparations with bovine blood intervening. Plot 4B has been adjusted byremoving the offset at 1125 nm from each of the spectra to remove themajor differences due to the sample to probe separation. Measurementswere made at 0.0, 0.25, 0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 nm from thetissue surface. As the probe moves further away from the tissue surface,the features seen when the probe was pressed against the tissue becomemuch weaker.

[0065] The absorbance can be used directly, but as can be seen fromFIGS. 2 and 3, there are offsets and slopes that in general confound theinformation coming from the absorbance related to the chemicalcomposition or tissue type. There are a number of ways to preprocess thespectra to remove these unwanted effects of shifted baselines and slopesfrom both the instrument and the biological system effects, along withthose of scattering from the biological. Such methods include, but arenot restricted to, first or second derivatives, normalization,autoscaling, multiple forms of baseline removal, mean centering (MC),multiplicative scatter correction, standard normal variant (SNV),Savitsky Golay smoothing, detrending, OSP, GLS filtering, waveletfiltering, FIR filtering, and combinations thereof, but not limited tothese options. The spectra can be untreated, using the raw absorbancemeasurements, or they can be pre-processed before further datamanipulation within the model building application. In one embodiment,the preprocessing option is standard normal variant combined with meancentering. In other embodiments, the preprocessing option is SNV with MCand detrending, or the spectra are pretreated by removal of the centralmean (MC-mean centering). In another embodiment, the preprocessing is aSavistsky-Golay smoothing first derivative.

[0066] For example, spectra of the specimens shown in FIG. 3 werepreprocessed using standard normal variant (SNV) preprocessing. Theresults are shown in FIG. 5, which clearly demonstrates the successfuluse of a preprocessing option to remove the scatter between thedifferent diseased tissue spectra, which can otherwise interfere withthe process analysis. To further enhance the information containedwithin the preprocessed spectra, one can use additional techniques. Forexample, FIG. 6 shows spectra preprocessed with both SNV and meancentering (MC). Comparing FIG. 5 to FIG. 6, it is clear that there are anumber of areas within the spectra that vary greatly with the additionalpreprocessing treatment of MC added to SNV. However, the regions in thespectrum that correspond to the chemical composition of interest withinthe lesion (in this case lipid pool concentration) are not easilyobtained by merely observing the results of the preprocessed data. Thus,the preprocessed digital data is typically processed further to obtain acorrelation between the data and the actual constituents within a bloodvessel wall, i.e., to determine whether a lesion is vulnerable or safe.

Processing Data

[0067] The digital data can be further processed using any or a varietyof discrimination algorithms (qualitative analysis) to determine thenature of the correlation between the constituents within the bloodvessel walls (as determined by an external means such as morphometrymeasurements or chemical analysis) and the spectral features obtained inthe NIR spectrum (the digital data). In other embodiments discussed infurther detail below, one can also make a quantitative analysis of theresults.

[0068] Useful discrimination algorithms use computerized mathematicalmodels developed by modeling the relationship between spectra and tissuestates of known tissue samples. These models are typically based onlarge amounts of patient data or ex vivo data simulating in vivo data.The mathematical models can be based upon chemometric techniques such asPartial Least Squares Discrimination Analysis (PLS-DA), PrincipleComponent Analysis with Mahalanobis Distance and augmented Residuals(PCA/MDR), and others such as PCA with K-nearest neighbor, PCA withEuclidean Distance, SIMCA, the bootstrap error-adjusted single-sampletechnique (BEST), neural networks and support vector machines, and othertypes of discrimination means.

[0069] The discrimination algorithm is applied to the digital data fromthe spectra of an unknown tissue sample in a blood vessel wall tocharacterize a particular location or point within the tissue. Forexample, the tissue can be characterized as being part of a lesion orplaque, and if so, whether this is a vulnerable or safe plaque asdescribed above. An output, such as a audible or visual representation,e.g., a graph or other output, can be provided to indicate to theoperator the characterization of the vascular tissue, including whetherthe illuminated blood vessel wall includes a plaque, and if so, whetherthe plaque is vulnerable or safe, or falls within one of the othercategories described above or simply whether the site of illuminationcontains a plaque that is vulnerable. The output can be based on one ormore thresholds, a continuous grading output as described herein, orboth types of output in the same system.

[0070] Many different types of discrimination algorithms can be used,from the basic form of a simple absorbance comparison between two ormore wavelengths, or analysis of large data matrices (raw orpreprocessed) with techniques based upon multiple linear regression,Principal Component Analysis (PCA) as described by Malinowski et al, in“Factor Analysis in Chemistry” John Wiley & Sons, New York, 1980. Thesemethods are used to obtain a metric to determine the nature of thediseased tissue from which the groups can be separated.

[0071] For example, one embodiment uses PCA, which enables the use oflarge amounts of spectral data information, which can be decomposed (andtherefore compressed) into a matrix of factors (or vectors, principlecomponents, latent variables, eigenvectors, etc.) that retain only thelargest variations expressed by all the spectra of the tissue type ofinterest, and a matrix of the resultant scores (or scalars, eigenvalues,etc.) used to scale the amplitude of each of the factor vectors. Theupper half of FIG. 7 is a schematic that depicts the PCA process ofspectral decomposition of the original spectrum into the Scores (S) andPrincipal Component (PC) matrices. As shown in the lower half of FIG. 7,the results are then analyzed by reconstructing the spectra from thecombination of the S and PC values and then minimize the differencebetween the comparison of the original to the reconstructed spectrausing the residuals (left over values from the comparison squared andthen summed).

[0072] In some embodiments, once a metric has been chosen, a thresholdis applied to determine the likelihood of whether the unknown tissuespectrum can be classified as a diseased tissue type or not (this is thethreshold that is avoided when using a threshold-less system asdescribed herein). Many methods can be used for this determination suchas a simple wavelength comparison technique using linear regressionlines, or more complex geometries such as Euclidean or Mahalanobisdistances as thresholds for more complicated separations. When analyzinglarge volumes of spectral data, PCA can be used and can also be combinedwith a variety of metrics for discrimination such as a linear regressionline, e.g., such as the one used in the partial leastsquares-discriminate analysis (PLS-DA) method described in Ericsson etal., “Multi- and Megavariate Data) Analysis: Principles andApplications” (Umetrics Academy, 2001), Euclidean distance, andMahalanobis distance as described by Marks et al in the followingarticles: Analytical Chemistry, 57, 1449, (1985); Analytical Chemistry,58, 379, (1985) and Analytical Chemistry, 59, 790, (1987). Otherelements besides the scores of the PCA decomposition can be used for ametric with a threshold, such as the soft independent modeling of classanalogies (SIMCA) described in Gemperline et al., Anal. Chem., 61:138,(1989), the bootstrap error-adjusted single-sample technique (BEST)described in Lodder et al., Appl. Spec., 42:1352, (1988), and otherdiscriminatory techniques which use the residual rather than the scoresfrom the reconstructed spectra.

[0073] In other embodiments, the original processed data (in the form ofa set of numbers, with one number for each point or location within ascanned tissue sample) is continuously graded using standard techniquesto provide a scale or value for each point without the use of athreshold. Thus, these methods utilize the raw scores, or the so-called“discriminant” based on the detected radiation, directly to provide acontinuous scale, rather than comparing the discriminant to a thresholdand providing a “yes/no” or other similar answer based on specificcategories.

[0074] One embodiment combines the scores of the separate classificationgroups, determined by spectral decomposition using partial leastsquares, and applying a threshold determined by maximizing theclassification separation between two or more groups to establish theboundary calculations termed the PLS-DA method.

[0075] In the embodiment in which a PLS algorithm is used fordiscrimination analysis (PLS-DA), the scores from the S matrix are usedto build the discrimination calibration model. A threshold can then beset to maximize separation of the scores in the model group from thosescores of the other group that were used in a binary representation. Forpredictions, an unknown spectrum is decomposed to the same S matrix, andif the score is above the threshold of the model then the sample is saidto be a member of the model class.

[0076] In other embodiments of the PLS-DA algorithm, no threshold isneeded and the resultant scores are displayed as a continuous gradingusing a standard technique to provide a scale or value for each pointwithout the use of a threshold. The threshold-less method directlyprovides a continuous scale, rather than comparing the discriminant to athreshold and providing a “yes/no” or other similar answer based onspecific categories.

[0077] In one embodiment, the raw PCA scores can also be used. Anotherembodiment of the model algorithm is a variation of PCA combined withstatistics of Mahalanobis Distance (MD), which can be also be augmentedwith the addition of spectral residuals (R) as described in a similarmethod developed by Gemperline et al., Anal. Chem., 62:465, (1990) (seeFIG. 7) where they combined PCA with SIMCA. This model embodiment,(PCA/MDR), developed by Duckworth, et al. of Galactic Industries andincorporated into their PLSplus/IQ™ Chemometric software uses PCA,SIMCA, and Mahalanobis Distances to determine the maximum discriminationof the model. In still another embodiment, the PCA residuals can be usedin the same way as in the method employed in SIMCA.

[0078] This model is formed by calculating the Mahalanobis distance onthe matrix of the PCA score values from the spectral decompositionaugmented with the mean centered sum squared residuals left over fromthe comparison of the reconstructed spectra to the original spectra usedin the model. This matrix is termed (Sr) in FIG. 7 and contains thescores for the description of the maximum variation of the spectracontained in the model augmented with the mean centered summed squaredresiduals left over from the PCA decomposition. These values are thensubjected to the Mahalanobis Distance calculations using the followingequation: $M = \frac{({Sr})^{T}({Sr})}{\left( {n - 1} \right)}$

[0079] in which M is the Mahalanobis matrix, T indicates the transposeis taken of the Sr matrix, and normalized by n, the total number ofspectra that were used to build the model. The Mahalanobis matrix isthen used to calculate the Mahalanobis Distance (D) for each Score (i)produced in the model.

D _(i)={square root}{square root over ((Sr _(i))M ⁻¹(Sr _(i))^(T))}

[0080] To make a comparison between all the members of the model, a rootmean squared group (RMSG) size is then calculated for the model. This isdone by obtaining the sum of all the squares of the predicted distancesfor each of the samples used in the model (D_(i)) normalized by thenumber of samples used in the model (n):${RMSG} = \sqrt{\frac{\sum\limits_{i = 1}^{n}\quad D_{i}^{2}}{\left( {n - 1} \right)}}$

[0081] The final calculation normalizes each of the Mahalanobis Distance(D_(i)) scores by the group normalization factor (RMSG). To determinewhether the normalized score values are within the model group oroutside of the model group values a boundary is set and in general thisboundary is set at 3 standard deviations away. The Mahalanobis Distanceis the first metric used to determine if an unknown sample is part ofthe model group or not.

[0082] The final decision as to whether a spectrum from an unknowntissue sample fits the particular model can be made using three metrics:(i) the fit of the unknown residual augmented scores to the MahalanobisDistance based upon the residual augmented scores of the model, (ii) theresultant residual of the unknown spectrum compared to a distributiontest such as a T-test or F-Test of the model residuals, and (iii) acomparison as to whether the unknown score fits within the range ofscores used to build the model.

[0083] To determine if the unknown sample is part of a specific group orcategory, the spectrum of the unknown is first preprocessed in the samemanner as the model and then decomposed into its PCA components plusresidual. The mean residual value obtained from the model is thensubtracted from the unknown residual to obtain the Sri value, where (i)represents the residual augmented scores vector for each of theindividual unknowns. This value is then applied to the followingequation to obtain the Mahalanobis Distance (D) for each of the (i)unknowns:

D _(i)={square root}{square root over ((Sr _(i))M ⁻¹(Sr _(i))^(T))}

[0084] This distance is then divided by the group RMSG value beforetesting the calculated Mahalanobis Distance of the unknown against thestandard deviation of the all the distances obtained from the model. Inone embodiment, three standard deviations (3σ) are used for theMahalanobis Distance boundary of the model. This is the first of three“flags” or indicators used as a disease index to ascertain whether theunknown sample falls within the model group (category) or not. Thesecond flag is obtained from the model residuals. A standarddistribution test such as a T-test, Chi-Squared, or F-Test is thenapplied to the model residuals at some level such as the 99% level, andthis value is used to determine if future predicted residuals fallwithin the model range. The third flag is the comparison of the unknownscore to the maximum and minimum score of the model or compared to astandard distribution test such as the T-test, Chi-Squared, or F-Testperformed at some level such as the 99% level. If any one of the threeflags is not present (false), then the sample is ruled not to fallwithin the model group/category being tested.

[0085] In another embodiment, the threshold is optimized during themodel testing stage against a test set of data that represents thelesions or normal tissues that are not part of the diseased state. Thisoptimized threshold is then used instead of the fixed three standarddeviations (3σ).

[0086] Although the use of thresholds has its benefits, their values areoften determined based on in vitro test results that may not accuratelyreflect in vivo spectral characteristics. Even if a threshold is basedon a large amount of in vivo data, a particular patient may not fitwithin the norms assumed when establishing the threshold. For such apatient, the best “control” may not be a predetermined threshold, but asample or location of his or her own tissue known to be normal (or atleast a safe lesion, if looking for vulnerable lesions). By using acontinuous scale to represent the set of numerical data representing thescanned locations within a tissue, e.g., in an artery, the new methodsand systems can provide the user, e.g., a physician, nurse, ortechnician with the opportunity to diagnose the vulnerability of aparticular lesion without a threshold, and thus without the risk of animproperly set threshold, which could cause an incorrect diagnosis.

[0087] Another advantage of the thresholdless display is that theoperator (e.g., physician, technician, or nurse) can make his or her owndecisions as to the trade-off between sensitivity and specificity, byapplying his or her own categories, criteria, or thresholds (which wouldotherwise be dictated by the system). The thresholdless display enablesthe operator to review a variety of discriminant values from multiplelocations within a given patient, and compare those values to each otherto make a diagnosis.

[0088] In some embodiments, the threshold and continuous gradingtechniques can be used together to provide a double-checking system.

[0089]FIGS. 12A and 12B illustrate the differences in the two methods.In the graph in FIG. 12A, a threshold is set to maximize the sum ofsensitivity and specificity of this system in which an algorithm reducesthe spectrum scanned to a number between −1.0 and 1.0. In this case, weare using the group Mahalanobis Distance as our metric of “scoring,” butother metrics may also be used. The peak of lipid-rich atheromas(vulnerable plaques) is at a value of about 0.2. The peak of othertissue types is at about −0.4. The threshold separating the two sets ofvalues is set at about −0.1. Thus, any sample locations having a valuegreater than −0.1 are designated vulnerable plaques.

[0090]FIG. 12B shows the same graph as in FIG. 12B, but without athreshold. Here, the system displays the values directly or by use of acontinuous grading system such as false color, a gray scale, or sound.In this system, the operator (e.g., a physician) reviews and interpretsthe values. Any number between −1.0 and 0.2 could be interpreted as asafe lesion, or a somewhat vulnerable lesion, any number between −0.2and 1 could be interpreted as a vulnerable lesion, or a somewhatvulnerable lesion. Values located somewhere in the middle can go eitherway based on the operator's experience and knowledge about a particularpatient and/or treatment in view of possible risks of treatment.

[0091]FIG. 14 shows a series of three graphs similar to FIG. 12A, inwhich each graph represents the results from a different patient. Thesame threshold is used for each patient, and as the graphs show, asingle threshold may not be optimal for all patients because ofinter-patient variation. In other words, the reflected radiation in onepatient may not mean the same thing in another patient. Displaying thedata directly enables the operator to decide upon a patient-specificthreshold after taking individual patient considerations into account.

[0092]FIGS. 15A to 15D are graphs showing chemometric prediction scores.This “score” can also represent the group Mahalanobis Distance oranother metric used to describe the state of the tissue. FIG. 15A issimilar to FIG. 12A. FIG. 15B is a graph showing the probability that agiven tissue sample is a lipid-rich atheroma. One can calculate theprobability that tissue with a given score is in a specific group (suchas lipid-rich atheroma vs. all other tissues) from the distribution ofchemometric scores of known populations of tissue samples. Suchcalculations can be made, for example, using a contrast maximizationalgorithm, and the results can be displayed in a grayscale. For example,one can use white for −1.0 and black for +1.0 (for chemometric scores),where 100% lipid-rich atheroma is black, and 0% lipid-rich atheroma iswhite. The probabilities can be displayed as an alternative to directdisplay of the chemometric scores. The probabilities provide more of avisual distinction or differentiation at the overlap between the twotissue distributions in the example shown in FIGS. 15A and 15B. Forexample, rather than using an entire grayscale to depict values from−1.0 to +1.0, the endpoints and values from +1.0 to +0.4 can be set toblack, and values from −0.4 to −1.0 can be set to white. Thus, thegrayscale covers only the values between +0.4 and −0.4, therebyproviding more visual contrast at these middle values near thethreshold.

[0093]FIG. 15C is a graph similar to FIG. 15B, but shows a straight-lineapproximation of the curve in FIG. 15B. FIG. 15D is a graph that doesnot show a probability, but just a grayscale value in a straight line(from 100 to 0 percent) in which every chemometric score is equallyimportant. These figures are just examples, and scalar data can bedisplayed in various ways known in the art.

Quantitative Analysis

[0094] In addition to the largely qualitative analysis discussed above,quantitative analysis can be used to predict the actual concentration ofspecified chemical constituents retained within a given location oftissue or lesion. For example, spectral information can be directlylinked to the actual chemical constituent using a variety of differenttypes of quantitative analysis based upon both univariate andmultivariate analysis techniques. Univariate methods include correlatingspectral peak heights or areas under the spectral curve to knownchemical quantities of interest within the tissue or lesions, using forexample least squares regression to develop a quantitative model.Another univariate method includes K-Matrix or classical least squares(CLS), which uses larger sections of the spectra (or the whole spectrum)regressed with respect to all of the chemical components within thespectral region (see, e.g., D. M. Haaland and R. G. Easterling inApplied Spectroscopy, 34, 539, 1980).

[0095] To avoid the complications that can arise when using univariatemodels, such as requiring knowledge of all the concentrations within theregion of the peaks (i.e., unknown concentrations will throw the modeloff), multivariate techniques may be more useful. In one multivariatemethod, multiple linear regression (MLR) (also termed P-Matrix orinverse least squares (ILS)) is used to build a model using only theconcentrations of the chemical components of interest (see, e.g., H.Mark, Analytical Chemistry, 58, 2814, 1986). While this technique allowsthe model to be built using only the known concentration without anyunwanted effects, the model is limited in the number of wavelengths thatcan be used to describe each of the components.

[0096] There are other multivariate techniques that combine the abilityto use large regions of the spectra to represent the constituents ofinterest (like that of the CLS model) with the ability of having tocontend with only the constituents of interest (like that of the MLRmodel). In one embodiment, principal component regression (PCR) is used(as described in Fredericks et al., Applied Spectroscopy, 39:303, 1985).This method is based upon spectral decomposition using PCA, followed bythe regression of the known concentration values against a PCA scoresmatrix.

[0097] Another embodiment that can be used to obtain actualconcentration values of lesion constituents based upon spectral datainvolves another multivariate algorithm termed partial least squares(PLS) analysis (see, e.g., P Geladi and B Kowalski, Analytica ChemicaActa, 35:1, 1986, and Haaland and Thomas, Analytical Chemistry, 60:1193and 1202, 1988). PLS is similar to PCR, however, both the spectralinformation and the concentration information are decomposed at thestart of the method and the resultant scores matrices are swappedbetween the two groups. This causes the spectral information correlatedto the concentration information to be weighted higher within the model.

[0098] The core of the PLS algorithm is a spectral decomposition stepperformed via either nonlinear iterative partial least squares(MPALS)(see, e.g., Wold, Perspectives in Probability and Statistics, JGani (ed.)(Academic Press, London, pp 520-540, 1975) or simple partialleast squares (SIMPLS) (Jong, Chemom. Intell. Lab. Syst., 18:251, 1993)algorithm.

[0099]FIG. 16 is a diagram representing the NIPALS decomposition of thespectral information represented by matrix X containing spectral dataand matrix Y containing concentration information (or binaryclassification information if using this method as a discriminationmeans). S and U are resultant scores matrices from the spectral andcomponent information, respectively, and PCx and PCy are resultantprincipal components (or latent variables/eigenvectors) for the spectraland component information, respectively. The other nomenclature in thefigure is for the number of spectra (n), the number of data points perspectra (p), the number of components (m), and the number of finallatent variables/eigenvectors (f).

[0100] Once the first decomposition for the spectral andconcentration/constituent data is made, resulting in a latent variableand score for each of the X and Y matrices, the scores matrix for thespectral information (S) is swapped with the scores matrix containingthe concentration information (U). The latent variables from PCx and PCyare then subtracted from the X and Y matrices, respectively. These newlyreduced matrices are then used to calculate the next latent variable andscore for each round until enough PCs are found to represent the data.Before each decomposition round, the new score matrices are swapped andthe new PCs are removed from the reduced X and Y matrices.

[0101] The final number of latent variables determined from the PLSdecomposition (f) is highly correlated with the concentrationinformation because of the swapped score matrices. The PCx and PCymatrices contain the highly correlated variation of the spectra withrespect to the constituents used to build the model. The second set ofmatrices, S and U, contain the actual scores that represent the amountof each of the latent variable variation that is present within eachspectrum. It is the S matrix values that are used in the PLS-DA model.

[0102] In one embodiment, the PLS method is used to predict that actualcompositions of the diseased tissue. For example the PLS algorithm canbe used to predict the chemical content directly or for example in theform of a percentage of lipid, fibrotic, calcific, cholesterol,macrophage, and water content within the probe scanning area. In anotherembodiment the PLS method can be used to predict the pH or temperatureof the diseased tissue or blood.

[0103] In certain embodiments, direct numbers or percentages can bereplaced by ranges or values from the prediction results of ranges forexample less than 10%, 1 1 to 25%, 26 to 40%, 41 to 60%, 61 to 75%, 76to 80%, and 81 to 100% and other combinations of ranges and values forprediction of lipid, fibrotic, calcific, cholesterol, macrophage, andwater content within the probe scanning area. In other embodiments, thePLS method can be used to predict various ranges of the pH ortemperature of the diseased tissue or blood.

[0104] Another embodiment treats prediction values above a certainthreshold as vulnerable (or life-threatening) and below the threshold assafe (or not life-threatening). For example, a vulnerable plaque may bedesignated as a region that contains 40% or more lipid content,therefore any predictions with respect to the lipid content would thenbe in the category of vulnerable. Multiple categories can be combinedand each assigned a threshold value. If the prediction scores are at orabove the threshold values for all the categories, then the result isthe sample is considered vulnerable.

Devices for Use in the New Methods

[0105] To perform the new methods, an improved apparatus is provided foranalyzing lesions and plaques in blood vessel walls in vivo. Theapparatus can also be used for in vitro analysis. The new apparatusincludes an external radiation source, such as a laser or other NIRradiation source for transmitting the incident NIR radiation within thewavelength range of 1100 to 1350 nm and at sufficient power. This sourcecan provide the desired NIR radiation region by scanning or bygenerating NIR radiation that spans the wavelength bands describedherein. In addition, the source can provide two or more singlewavelengths within this range similar to filter based NIR instruments.The radiation can be delivered sequentially or simultaneously. A varietyof NIR sources can be used to provide the required incident NIRradiation. For example, NIR spectra can be obtained from human bloodvessels, such as the coronary arteries or aorta, using light sourcessuch as tunable semiconductor lasers or solid-state lasers,fiber-coupled systems such as Raman amplifier lasers, or super continuumfiber lasers and other light sources such that the wavelengths can bescanned. Alternatively, the light source may produce spectral bands thatenable simultaneous illumination of the specimen with all requiredwavelengths. Tuning and/or spectral detection must occur rapidly (<1second) to avoid motion artifacts within the arteries. Monochromaticfixed-wavelength sources such as lasers, LED's, semiconductor diodelasers, DFB, can also be multiplexed to serve as the illumination sourcefor spectroscopic measurement.

[0106] Radiation is carried from the NIR source to the blood vesselwalls via any of a number of types of fiber optic catheters or probesoperatively connected to the NIR radiation source (see, e.g., Tearney etal., U.S. Pat. No. 6,134,003; Crowley et al. (BSC), U.S. Pat. No.5,588,432; and Colston, et al, U.S. Pat. No. 6,175,669. For example, thecatheter can have a single fiber optic core. A radiation directing orfocusing mechanism can be mounted to the distal end of the catheter toenable the operator to direct or focus the NIR radiation onto a desiredtarget on a blood vessel wall. The focusing mechanism should be adaptedto compress the incident radiation beam from the transmitting catheteronto a small spot on the tissue surface to be analyzed. NIR radiationreflected by the tissue of the blood vessel wall can be directed into areceiving optic fiber or fibers to provide a convenient, cost-effectivemeans for directing the light reflected from the specimen to thespectroscopic measurement device. Additionally, the apparatus includesone or more detectors present at the distal portion of the catheter forrapidly detecting the radiation reflected or scattered back from theblood vessel wall being illuminated.

[0107]FIG. 1 illustrates one of several embodiments of an apparatus 10that can be used to carry out the present invention. Depending upon thespecific wavelengths used, the apparatus is optimized in a manner toenhance the performance within those bands. This includes, but is notrestricted to, optimizing the optical fiber cutoff range, thereflectivity of director (mirror) substrate, the addition of lenses, thematerial and other properties of the sheath, and other apparatus itemsthat can be changed to optimize the signal received. More particularly,the apparatus 10 includes a fiber optic probe or catheter, generallydesignated by reference numeral 12. The catheter 12 has a distal end 14.The distal end 14 of the catheter can also include an optical aperture16 through which NIR radiation is directed and/or focused (viaredirecting and focusing means 15). This aperture can be centrallylocated or directed to one side of the distal end (as shown).

[0108] An optical fiber or fiber optic bundle 20 is located withincatheter 12. The fiber optic bundle 20 is operatively connected to a NIRradiation source 30 and detector source 40 (see FIG. 1). The NIR source30 is particularly adapted for generating multiple (e.g., 2, 3, 4, ormore) single wavelengths or a wavelength band of any 1, 2, 5, 10, 15,20, 30, 40 or more nm within the overall wavelength range of from 1100to 1415 nm.

[0109] An individual NIR radiation detector 40 includes one or moredetectors present at the distal portion of the catheter, such as leadsulfide detectors, InGaAs, Silicon, Ge, GaAs, indium antimonide detectorcooled with liquid nitrogen, e.g., singly or in an array, for rapidlydetecting the radiation reflected or scattered back from the bloodvessel wall being illuminated. Catheter 12 is inserted into the patientvia a peripheral vessel and moved to the desired target 52 area (lesion)using standard techniques and methods. Then, NIR radiation within the1100 to 1415 nm wavelength range from source 30 is directed along thetransmitting fiber optic bundle 20 to the fiber optic catheter 12.There, the NIR radiation from source 30 is projected as an incident beam17 through the optical aperture 16 onto a blood vessel wall 51 (incidentlight beam is depicted in full line arrows).

[0110] A significant portion of the incident NIR radiation is projectedonto point P of the blood vessel wall 51. The same catheter or probethat illuminates the blood vessel walls is typically also used tocollect radiation reflected from the target (the so-called “reflectancespectrum”). For example, as shown in FIG. 1, radiation is reflected backinto catheter 12 through aperture 16. The scattered, reflected radiationis shown at dash line arrows 18. Catheter 12 directs the reflectedradiation so that it falls upon detector 40 via the radiationredirecting and focusing means 15 and a beam splitter 22.

[0111] This reflected spectral information must be processed to obtainuseful information. As shown in FIG. 1, detector 40 is connected to ameans 42 for preprocessing, processing, and analyzing the detectedspectra and producing the analyzed results as a functional color schemeor other method to indicate whether the lesion is included or excludedfrom the model thereof. Specifically, the analysis is completed overselected wavelength region or regions of the incident NIR radiationdirected upon the tissue. Thus, reflected radiation within thewavelength range from 1100 to 1415 nm is analyzed or the 1100 to 1415 nmradiation coupled with radiation in the 1650 to 1780 nm region isanalyzed. To achieve this end a computer can be used that includesappropriate analytical algorithms as discussed herein.

[0112]FIG. 13A shows a schematic of a computer screen of a display in asystem 110 that uses a threshold. The computer screen 120 shows basicpatient information 122, the date and time of a scan 124, and an X-rayview 126 of a catheter 125 within a patient (here in the chest, note thelight gray ribs). In addition, screen 120 shows a digitized longitudinalview of an artery being scanned 128, and a digitized cross-section of aparticular section of an artery 130. The cross-section 130 is separatedinto 8 segments, while the longitudinal view of the artery has 7×26segments (showing an artery sliced longitudinally along the wall andthen opened flat).

[0113] Because a threshold has been set, the spot at 128 a with 11segments all having the same color or shade of gray indicates a portionof diseased tissue, e.g., a lesion. The spot at 128 b has all 13segments the same color or shade of gray as 128 a, indicating anotherlesion. In cross-section 130, three segments of the “ring” are a lightcolor or light gray, and indicate a lesion. Five segments of the ringare a dark color or dark gray, indicating that the rest of thecross-section is normal tissue free of lesions.

[0114]FIG. 13B shows a schematic much like the one in FIG. 13A, but fora system that does not provide or set a threshold. Here, screen 120shows a digitized longitudinal view 134 having 8×50 segments, and a keyor scale 135 to show the colors or shades of gray corresponding to thespectral values. The cross-sectional view 136 has eight segments, butwith no two predetermined colors or shades of gray to designate normalor diseased tissue. Instead, cross-section 136 shows a range of colorsor shades of gray for different segments of the artery. For example, thesegment at 136 a is dark, indicating that it is likely to be a safelesion or normal tissue segment. The segment at 136 b is very lightindicating a portion of tissue that is diseased, e.g., has a lesion thatis likely to be highly vulnerable. The other segments indicate generallyhealthy segments, with a possible lesion or somewhat vulnerable lesionat segment 136 c.

[0115] The longitudinal view 134 shows a very light section of about 25segments indicating a small, but highly diseased portion of tissue 134a, e.g., a highly vulnerable lesion. The rest of the segments beinggenerally darker indicate healthy tissue, with a slightly lighter areaof about 30 segments, indicating a possible lesion at 134 b, but afairly safe lesion that should be monitored to see if it progresses intoa vulnerable plaque in the future.

[0116] To convert a set of numbers, e.g., a range of chemometric valuesin a set, to a grayscale or color scale, one can use a standardtransformation, e.g., a linear transformation, to transform those valuesto a numerical scale (e.g., 0 to 255 for an 8-bit per pixel display),and then map the chemometric prediction values to a specific grayscaleor a color scale using standard techniques. For example, one can use thegrayscale ramp from black to white. Black is normally the low end of therange of values, and white is the high end. The map from black to whitefor each point in a set of values is usually just a linear ramp, for avalue (v) which varies from vmin to vmax, the specific gray tone for agiven point is then (v−vmin)/(vmax−vmin). For a color map, the mostcommonly used color ramp is often referred to as a “hot-to-cold” colorramp. Blue is chosen for the low values, green for middle values, andred for the high. One can ramp between these points, or can add thecolors cyan and yellow, to provide additional transitions along thislinear ramp.

[0117] The data can also be plotted in, e.g., a Microsoft® Excel®spreadsheet using a so-called “surface map” charting feature.Commercially available software such as MatLab® also has built-infunctions to display an array of data as a grayscale or false colorscale.

Experimental Models

[0118] The new methods are based upon studies in which NIR spectroscopywas used to examine human aortic samples through whole blood. Thesamples contained both normal and diseased tissue pertaining to variousstages of atherosclerotic plaque growth. The resulting reflectancespectra were converted to absorbance spectra as a function of the log of1 /Reflectance and were analyzed using chemometric techniques thatprovide a means for modeling the data in such a manner to maximize thespectral information pertaining to the lipid pool content as obtainedthrough blood. Thus, the determination of plaque vulnerability is based,at least in part, on the nature of the lipid pools withinatherosclerotic plaques, and their spectral patterns when covered bythicker or thinner fibrous caps.

[0119] Computer modeling identified the NIR wavelength range from 1100to 1415 nm as unique for use in predicting the nature of the lipid poolcontent in human atherosclerotic plaques. In addition, it was determinedthat sufficient information could be obtained by illuminating the targettissue with two or more single wavelengths, multiple combinations ofsingle wavelengths or one or more narrow bands of wavelengths, eachcovering as few as 1, 2, 4, 10, 15, 20 or 30 nm (or up to 100 nm ormore), within this range of 1100 to 1415 nm or with a combination of the1100 to 1415 nm range with the about 1600 to 1780 nm range.

[0120] Two chemometric discriminant techniques were used in this study,PCA/MDR and PLS-DA, both based upon PCA, but there are many otherdiscrimination methods that can be used to build the prediction modelssuch as those described above. As discussed above, PCA is a linearregression method that decomposes the spectral information into asmaller set of vectors (principal components, factors, eigenvectors,latent variables, etc.) and scalars (scores, eigenvalues, etc) thatdescribe the variations of the spectral components while leaving outrandom noise components. Both methods require a training set ofrepresentative samples, but differ in the assembly and treatment ofthose samples. The training set is used to build a mathematicalrelationship that recognizes similar qualities for a single (or more)classification group, and then the model acts as a screening tool forall subsequent samples tested. The segmentation into the variousclassification groups was based upon the morphology and morphometryresults of the tissue samples. Both discrimination methods are basedupon the ability of the samples containing mainly lipid pool (LP) to bedistinguished from a combined set of mainly fibrotic tissue (FIB) andcalcific tissue (CAL) specimens, and also from normal tissue (NML)through blood.

[0121] In the first case, PCA/MDR, the PCA scores from the LP specimenspectra are combined with the remaining noise components (residuals) anda Mahalanobis Distance (MD) statistic is applied to all the scores,obtaining a model based upon the range of the distances that form anellipsoidal centroid around all the scores (see FIG. 7). Only one groupis required to build the model. The other groups are used to establishthe relative “goodness of fit” as to how well the model recognizes LPsamples and discriminates against the other diseased tissue samples.

[0122] In the second case, PLS-DA, there is a requirement for two groupsto be established that are the hardest to distinguish between. These twogroups are required to build the model, and the method calculates a“best-fit” line that separates the PCA scores of the LP specimens fromthe PCA scores of the second group, and in this case the group is thecombined FIB and CAL specimen. FIG. 9 is a plot of the results of thePLS-DA model of PCA scores between 1100 and 1350 nm. In this case, alinear discrimination line was used to separate the principal componentscores for factor 3 (PC3) from the principal component scores fromfactor 4 (PC4), where a “factor” is the vector describing the largestinfluence remaining in the spectral matrix. A best-fit line was drawn tobest separate the scores from the lipid pool samples (LP) from those ofall other samples (in this case they were the calcific (CAL) and thefibrotic (FIB) sample scores).

[0123] To determine how well a model works, separate groups of samplesclassified by morphology were set aside and used as a validation testset, LP samples not used to build the model were used in the validationstep. Sensitivity (SENS) is the ability to recognize a sample that isthe same as the model, lipid pool samples in this case and Specificity(SPEC) is the ability of the model to disregard samples that are not thesame as the model (NML, CAL, FIB, etc). The reported SENS and SPECvalues were comprised of the percentage of the number of LP samples thatpassed (in the case of SENS) or failed (in the case of SPEC), and in thecase of the PCA/MDR model there is a the three parameter tests of themodel: (1) the model Mahalanobis Distance boundaries, (2) the scorelimits, and (3) the residual limits. Both the SENS value and the SPECvalues should be close to 100% for the better models, indicating thatthe model can classify all of the LP samples as LP and reject all othergroups tested.

[0124] As discussed in further detail in Example 1 below, human aortatissue samples were analyzed and characterized using standard histologyand morphology techniques. As described in Example 2, each sample wassubjected to NIR spectroscopy and the resulting reflectance spectra werecollected, analyzed, and categorized into large data sets (Example 3).

EXAMPLES

[0125] The invention is further described in the following examples,which do not limit the scope of the invention described in the claims.

Example 1 Human Aorta Tissue Sample Preparation

[0126] Human aortic tissues were obtained from the thoracic or abdominalregion of deceased patients and removed 24 hours or less after death.The tissue samples were stored at 4° C. in saline solution or aphosphate buffered 0.9% saline (PBS) solution. The aortas containedmultiple, advanced atherosclerotic lesions unless specifically chosen asnormal samples. The aortic fat was removed from the exterior of theaortic vessel. Each specimen (plaque or normal) was removed from theaorta using a 2 cm block template with the plaque or normal regioncentered within the block and kept moist until the near infrared (NIR)experiments were finished. The plaque sample was oriented such that themaximum lipid pool area was horizontally located in the center of thespecimen. Once the NIR spectroscopy experiment was performed, the tissuespecimen was placed in a sample holder with the upper left hand comer ofthe tissue laid in the upper left hand comer of the sample holder. Adigital photograph was taken of the entire specimen.

Example 2 Analytical Sample Setup

[0127] A NIR spectrum was obtained from each human aortic sample using aFOSS® NIRSystem®, model 6500, with a hand held ½ inch fiber optic probeattachment held in place on a vertical stage adjustment platform.VISION® software version 2.11 data acquisition software was used tocollect the spectrum and convert the data from Reflectance to Absorbanceunits. The recorded wavelength range spanned from 400 nm to 2500 nmacquiring 32 co-added scans at a 10 nm resolution. Data acquisition took45 second per sample.

[0128] As shown in FIG. 10, an experimental setup was created to holdthe fiber optic probe in place at all times during the experiment. AFOSS® probe 100 was located vertically above the plaque tissue sample110, which is positioned on a rubber mat or pad 120. The sample wasilluminated through bovine blood 130. The distance between probe 100 andthe sample 120 is the “blood depth n.”

[0129] A Z-stage micrometer (not shown) was added to the platform toregulate the vertical adjustments of the FOSS probe 100 and to measurethe distance from the surface of the sample (zero point) and upwards at0.25, 0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 mm increments. The specimen wasplaced on the center of a 4×4×0.5 cm³ black rubber pad 120. The aorticsample 110 with the intimal side facing up was fixed over the rubberwith pins at all four comers, and then placed in a small PYREX® dish 140that holds up to 100 ml of liquid. The sample dish was then placed intoa recirculating heated water bath set to 38° C. as monitored by amercury thermometer.

[0130] The optical FOSS® probe was adjusted until it contacted the topof the plaque (or normal) sample and the micrometer was set to zero (0).For each of the probe placements the data acquisition was performedtwice without moving the probe. The sample was first scanned in air atthe zero mark and then the probe was raised to 2 mm and scanned again. Asolution of 0.9% saline (warmed to 38° C.) was then poured into theglass dish immersing both the sample and probe to a height well above 10mm. The sample was scanned and then the saline was removed using asyringe, without moving the sample or the dish.

[0131] Fresh 40% hematocrit bovine blood anti-coagulated with heparin(warmed to 38° C.) was placed in the PYREX® container up to a height ofabout 15 mm. The probe was lowered back to the zero position (0 mm onthe micrometer) and the sample was then scanned in the bovine blood. Theprobe (using the micrometer) was moved to 0.25 mm and the sample wasagain scanned, the process repeated for 0.5, 1.0, 1.5, 2.0, 2.5 and 3.0mm distances between the probe tip and the tissue sample.

[0132] All the digital data files were converted from Reflectance toAbsorbance and then stored. All of the NIR spectra were visuallyinspected for data consistency using the VISION® software. Certain datasets were then excluded from further use based on 1) all spectra withina data set did not change with respect to the depth changes, and 2) somesamples within a data set changed with respect to depth and other didnot.

[0133] The results are shown in FIG. 4A, which displays a lipid poolplaque sample at various sample-to-probe distances. FIG. 4B is a plot ofthe same data shown in FIG. 4A, but the data was offset (normalized) bythe absorbance at 1125 nm for each spectrum, showing differences thatexist between the samples.

Example 3 Morphological and Morphometric Analysis to Create Data Sets

[0134] After processing by NIR spectroscopy, the samples were fixedovernight in a 3% formalin solution. Samples that showed gross evidenceof calcification were decalcified in 5% HCl in formalin for 4 hoursfollowing formalin fixation.

[0135] Sectioning of the specimens was done by hand with a scalpel intothree segments, and those three segments were divided (again by hand)into three subsections and placed within paraffin for sectioning. Theslides were made from the approximate middle of the subsections at 500to 750 microns into the block. For the suspected plaque lesion samples,several ribbons were taken from the following subsections: thecentermost piece, and the four edge pieces that touch the center piece.FIG. 11 is a schematic diagram of the histology layout used forprocessing the tissue specimen. Nine segments (labeled A1 through C3)were removed, with the segments subtended by the FOSS® probeillumination circle (center circle) of most importance (segments A3, B1,B2, B3, C1) in the analysis. The stars indicate the approximate centerof each of the segments from which the slices for slides were taken toavoid edge effects from the gross cutting. For the normal specimens onlythe center, and two outer pieces were analyzed (see FIG. 11, segmentsA3, B2, C1).

[0136] Five slides were made for staining purposes, two using H&E andtwo using Trichrome—Elastin and one spare blank slide. There were 25slides for each plaque sample and 15 slides for each normal sample. Onestaining set was produced for planimetry analysis using all the slices,and another set was made for initial morphology analysis using only thecentermost B2 slices.

[0137] The morphology (or overall description) for each specimen wascharacterized from the histology of the stained slides for each of thesubsections. Within each slide subsection, only the area illuminated bythe FOSS® probe was considered in the analysis (see central circle inFIG. 12). The probe diameter was 12 inch (1.27 cm), but the actuallyprojection area used for histology was approximated as a 1 cm diametercircle within the center of the tissue sample to approximate tissueshrinkage and light scattering events during the NIR analysis.

[0138] The initial morphology of the samples was performed based uponaccepted descriptions of normal and vulnerable plaque tissuesestablished by Virmani et al at the Armed Forces Institute of Pathology(AFIP) Arteriosclerosis. Thromb. Vasc.Biol., 2000, 20:1262. Plaquesamples were further separated into lipid pool, fibrous, or calcificplaques with the extremes being classified as having the majority of themain constituent within the probe illumination area. Approximate lipidpool width and depth along with average and minimum cap thickness werealso recorded for most of the lipid pool samples.

[0139] Computerized morphometry (planimetry) was performed for all thestained tissue slices. The morphometric analysis was used to determinethe Total Plaque Area (subtended by the FOSS probe) then separated outinto Total Lipid Pool Area, % Lipid Pool Area to Total Plaque Area, andCap Thickness (measured at the thinnest region only). Further, capthickness measurements were obtained for all the data samples thatcontained lipid pools. The average cap thickness was measured over thecenter 10 mm of the centermost section to provide an average capthickness value.

[0140] The normal samples were also analyzed to determine if the tissueof the outer subsections were free of disease. Any start of disease orlipid pool found in the segments disqualified the specimen as normal.The normal samples were from individuals that ranged in age from asyoung as 29 up to 87 years of age. Most of the normal samples came fromindividuals that did not have any disease.

[0141] Samples selected for the study represented the diseased plaquesof LP, FIB, or CAL along with the NML tissue, all within the FOSS probesubtended area. The LP samples selection was based upon the size of thetotal plaque area (only the larger plaques were used to build the model)and the ratio of the mean Cap Thickness to the percentage of LP area andthe percentage of the LP area to total plaque area (smaller caps onlarger LP plaques were ranked as good LP extreme samples).

Example 4 Data Set Development

[0142] A subset of the fall data set was formed using the histologyanalysis as described in Example 3.

[0143] The data was segmented out by the two pathologists and thenclassified as extreme classification samples. The extremes wereclassified as consisting predominately of one disease component, withoutregard to the thickness of the cap on the lesion. The full datasetcontained a total of 207 samples with 194 usable. The files used formodeling were chosen using two criteria: (i) being in the top ⅓ largestplaques in the sample set, and (ii) the average cap thickness (inmicrons), in a ratio to the percent lipid pool area to the totaldiseased plaque area. This threshold was set to be less than 18,determined as the middle of the histogram plot of all the data. Thisfilter process resulted in a total of 33 extreme plaques and 27 Normal(NML) samples. The plaque samples were further classified as 16 LipidPool (LP), 8 Calcific (CAL), and 9 Fibrotic (FIB).

[0144] To increase the number of plaque samples in the model, 2lipid-filled disrupted plaque (DP) samples were added to the calibrationset and another 2 lipid-filled DP samples to the validation set. Thisincreased the number of LP samples to 10 samples per each set,validation, or calibration. These DP plaque samples were chosen becausethey were large in size, had very thin to non-existent caps, and stillretained a large amount of pooled lipid within the plaque.

Example 5 Determination of the Wavelength Range of 1100 to 1415 nm

[0145] The final method used to evaluate the data sets was the linearregression model based upon the Mahalanobis Distance and the scores fromthe PCA decomposition, the PCA/MDR method. Within the different regionstested, most predictive regions were found to cluster in the region fromabout 1100 to 1415 nm, and more particularly from about 1150 nm to 1350nm, about 1175 to 1280, and about 1190 to 1250 nm. The data were firstpreprocessed for scatter removal using Standard Normal Variant (SNV)with Mean Centering (MC). Overall the SNV with MC preprocessing optionprovided the best SENS and SPEC results for all models tested ascompared to no preprocessing, mean centering, first derivative, secondderivative, and other preprocessing options tested. The best bandregions found for the dual 30 nm test were from 1175 nm to 1205 nmcombined with 1310 nm to 1340 nm, and 1145 to 1175 nm combined with 1250to 1280 nm. 88% of the LP samples fit the model first model with 86% ofthe FIB and CAL samples, and 100% of the NML samples were rejected bythe model. For the second set of bands, 90% of the LP samples fit themodel and 86% of the FIB and CAL samples, and 86% of the NML wererejected by that model.

[0146] Specifically, the PCA/MDR model was used to determine the rangeof configurations that could be used for discriminating plaquescontaining mainly lipid pool from other disease types and alsonon-disease types. A minimum of two selected wavelengths or one or morenarrow wavelength bands (e.g., one wavelength and one narrow band)within the wavelength range of 1100 to 1415 nm are required. Eithercontinuous or discontinuous wavelengths or regions can be used to buildthe model without restriction to size. Many PCA/MDR models were madeusing the data files for the specimens containing mainly lipid pool anddiscriminated against fibrotic and calcific lesions together, and thenthe normal tissue, while also encompassing the variations of the spectraas seen through blood as the probe is progressively moved away from thetissue. The assessment of the models that were tested were obtained fromthe prediction results of all the plaque types, with a tissue to probeseparation value from 0.0 mm to 3.0 mm (see FIG. 4), which represent theexpected range of distances between the examination device and thetissue being examined in vivo. Larger distances can also be examined.

[0147] The calibration model testing process was repeated eight timeswith randomly chosen lipid pool samples each time. Each repeatedcalibration process was averaged over all the regions tested providingthe Mean Performance results. These results are displayed in the“projection plots” in FIGS. 8A to 8C for model spectra that werepreprocessed using Standard Normal Variant (SNV) and Mean Centering(MC). From those results, the regions from 1100 nm to 1415 nm and acombination of bands from the 1100 nm to 1415 nm region in conjunctionwith bands in the about 1650 nm to 1780 nm regions (e.g., 1650 to 1730nm) resulted in many useful models. Each model prediction result wascaptured as the number of the test lipid pool samples predicted to bepart of the model (i.e., vulnerable plaque), and the number of calcificand fibrotic, and then normal tissue samples (i.e., safe plaque), thatwere excluded from the model, and reported as Percent Sensitivity andPercent Specificity1 and Percent Specificity2, respectively.

[0148] These results were combined as a single metric to generate FIGS.8A to 8C, in which the x-axis plots the beginning value of the first 30nm window region and the y-axis plots the beginning value of the second30 nm window region used in model.

[0149] To generate FIG. 8A, two moving “windows” of 30 nm each weretested spanning from 1100 nm to 1850 nm at 15 mm intervals. The X-axisplots the beginning value of the first 30 nm window region and theY-axis plots the beginning value of the second 30 nm window region usedin the model. Only those results greater than 1.8 (equivalent to aminimum response of 60% sensitivity with respect to lipid pool samplesnot used to build the model, 60% specificity with respect to calcificand fibrotic samples combined, and 60% specificity with respect to thenormal samples) were retained. The best result would be 3.0corresponding to 100% sensitivity and specificity for all groups.Specific areas of high Mean Performance occur in the lighter shadedareas and in particular in the regions from 1100 nm to 1415 nm, and moreparticularly, 1100 to 1350 nm. Areas of higher Mean Performance occur inthe most lightly shaded (highlighted) areas and in particular in theregion from 1190 nm to 1250 nm.

[0150]FIGS. 8B and 8C are two projection plots of the mean performanceusing two separate 30 nm bands as in FIG. 8A, but tested at a higherresolution of 2 nm intervals and spanning the region from 1100 nm to1350 nm. The X-axis plots the beginning value of the first 30 nm windowregion and the Y-axis plots the beginning value of the second 30 nmwindow region used in model. A higher threshold was applied to this data(compared to FIG. 8A) so that only those models where the minimumretained value for sensitivity with respect to lipid pool samples was70%, for specificity with respect to calcific and fibrotic samplescombined was 70%, and for specificity with respect to the normal sampleswas 70%. The best result would be 3.0 corresponding to 100% sensitivityand specificity for all groups and the minimum would be 2.1.

[0151] In FIG. 8B, the data was first pretreated using a Savitsky-Golaysmoothed first derivative followed by mean centering, and in FIG. 8C thedata was first pretreated using Standard Normal Variate with meancentering. The highest Mean Performance for FIG. 8B occurred in thelighter shaded areas and in particular in the regions from about 1150 nmto 1250 nm, and for FIG. 8C, the region spanned from about 1175 nm to1280 nm.

[0152] For example, one embodiment uses two 30 nm band regions that arediscontinuous in the region from 1190 nm to 1290 nm as seen in FIGS. 8Band 8C by the grey areas within the very light white region from 1190 to1290 nm. Another embodiment uses bands from 1175 to 1205 nm with 1310 to1340 nm. Alternatively, another embodiment uses bands from 1145 to 1175nm with 1250 to 1280 nm.

Other Embodiments

[0153] It is to be understood that while the invention has beendescribed in conjunction with the detailed description thereof, theforegoing description is intended to illustrate and not limit the scopeof the invention, which is defined by the scope of the appended claims.Other aspects, advantages, and modifications are within the scope of thefollowing claims.

What is claimed is:
 1. An in vivo method for characterizing tissue in ablood vessel wall, the method comprising (a) illuminating a tissue inthe blood vessel wall with any two or more single wavelengths or one ormore narrow wavelength bands of near-infrared radiation within awavelength range of about 1100 to 1415 nm; (b) detecting radiationreflected from the tissue having a wavelength of from about 1100 to 1415nm; (c) processing the detected radiation to characterize the tissue inthe blood vessel wall; and (d) providing an output indicating the tissuecharacterization.
 2. The method of claim 1, wherein the one or morenarrow wavelength bands each span about 1.0 nm to about 100 nm withinthe wavelength range of 1100 to 1415 nm.
 3. The method of claim 1,wherein two single wavelengths are used.
 4. The method of claim 1,wherein two narrow wavelength bands, each spanning 1.0 nm to 30 nmwithin the wavelength range of 1100 to 1415 nm are used.
 5. The methodof claim 1, wherein at least one narrow wavelength band and at least onesingle wavelength are used.
 6. The method of claim 1, wherein thewavelength range is about 1100 to 1350 nm.
 7. The method of claim 1,wherein the wavelength range is about 1150 to 1250 nm.
 8. The method ofclaim 1, wherein the wavelength range is about 1175 to 1280 nm.
 9. Themethod of claim 1, wherein the wavelength range is about 1190 to 1250nm.
 10. The method of claim 1, further comprising illuminating thetissue in the blood vessel wall with any two or more single wavelengthsor one or more narrow wavelength bands of near-infrared radiation withina second wavelength range of about 1600 nm to 1780 nm; and furtherdetecting radiation reflected from the tissue having a second wavelengthof from about 1600 nm to 1780 nm.
 11. The method of claim 10, whereinthe second wavelength range is about 1650 to 1730 nm.
 12. The method ofclaim 1, wherein the blood vessel is filled with blood, and the tissuein the blood vessel wall is illuminated through the blood, and reflectedradiation is detected through the blood.
 13. The method of claim 12,wherein the blood in the blood vessel is occluded.
 14. The method ofclaim 13, wherein the blood is occluded by a balloon.
 15. The method ofclaim 1, wherein the blood vessel is filled with a biocompatible liquid,and the tissue in the blood vessel wall is illuminated through thebiocompatible liquid, and reflected radiation is detected through thebiocompatible liquid.
 16. The method of claim 1, wherein the processingcomprises the use of qualitative chemometric discrimination algorithms.17. The method of claim 16, wherein the algorithms utilize partial leastsquares-discriminate analysis (PLS-DA), principle component analysiswith Mahalanobis Distance (PCA-MD), or principle component analysis withMahalanobis Distance and augmented residuals (PCA/MDR).
 18. The methodof claim 16, wherein the processing comprises classifying the detectedradiation into two or more categories.
 19. The method of claim 1,wherein the processing comprises the use of quantitative chemometricalgorithms.
 20. The method of claim 19, wherein the algorithms utilizepartial least squares (PLS) or principal component analysis (PCA). 21.The method of claim 1, further comprising preprocessing the detectedradiation to remove spectral information not related to acharacterization of the tissue.
 22. The method of claim 1, wherein theblood vessel is an artery.
 23. The method of claim 1, wherein the bloodvessel is a coronary artery.
 24. The method of claim 1, wherein thetissue comprises a lipid pool.
 25. The method of claim 1, wherein thetissue comprises a lipid pool and a thin fibrous cap.
 26. The method ofclaim 1, wherein the tissue comprises a lipid pool and a thick fibrouscap.
 27. The method of claim 1, wherein the tissue comprises fibrotic orcalcific tissue.
 28. The method of claim 1, wherein the output providesa continuous grading of the scanned tissue.
 29. The method of claim 1,wherein the output categorizes the scanned tissue into two, three, ormore different categories of lesions.
 30. The method of claim 1, whereinthe output categorizes the scanned tissue as either healthy or avulnerable plaque.
 31. The method of claim 1, wherein the output is agraphical representation of the signals corresponding to the reflectancespectra.
 32. The method of claim 1, wherein the output is a color schemeof the tissue characterization.
 33. The method of claim 1, wherein theprocessing comprises applying a threshold to determine whether thescanned tissue is diseased or not.
 34. The method of claim 33, whereinthe processing comprises applying a threshold determined by optimizingthe separation between two or more groups to establish a boundarycalculation that determines whether the scanned tissue is diseased ornot.
 35. The method of claim 33, wherein the output categorizes thetissue as lipid-rich or not.
 36. The method of claim 1, wherein theoutput categorizes the tissue as lipid-rich, calcific, fibrotic, normal,or other.
 37. The method of claim 33, wherein the output categorizes thetissue as TCFA or not.
 38. The method of claim 33, wherein the outputcategorizes the tissue as a vulnerable lesion or not.
 39. The method ofclaim 1, wherein the output categorizes the tissue as diseased or notwithout applying a threshold.
 40. An apparatus for scanning andcharacterizing tissue in vivo, comprising: a near-infrared radiationsource that generates radiation comprising any two or more singlewavelengths or one or more narrow wavelength bands of near-infraredradiation within a wavelength range of about 1100 to 1415 nm; one ormore radiation conduits for transmitting radiation from the radiationsource to the tissue and for receiving radiation not absorbed by thetissue; a radiation detector that collects radiation not absorbed by thetissue across a wavelength range of substantially 1100 to 1415 nm; aprocessor that processes the collected radiation to characterize thetissue; and an output device that indicates the characterization of thetissue.
 41. The apparatus of claim 40, wherein the near-infraredradiation source generates a wavelength range of about I 100 to 1350 nm.42. The apparatus of claim 40, wherein the near-infrared radiationsource generates two narrow wavelength bands, each spanning 1.0 nm to 30nm within the wavelength range of 1100 to 1415 nm.
 43. The apparatus ofclaim 40, wherein the near-infrared radiation source generates awavelength range of about 1150 to 1250 nm.
 44. The apparatus of claim40, further comprising a near-infrared radiation source that generatesradiation comprising any two or more single wavelengths or one or morenarrow wavelength bands of near-infrared radiation within a secondwavelength range of about 1600 to 1780 nm.
 45. The apparatus of claim40, wherein the output device provides a graphical representation of theradiation diffusely reflected from the scanned tissue.
 46. The apparatusof claim 40, wherein the output device provides a functional colorscheme of the scanned tissue.
 47. The apparatus of claim 40, wherein theoutput device provides a continuous grading of the scanned tissue. 48.The apparatus of claim 40, wherein the processor and output devicecategorize the scanned tissue into two, three, or more differentcategories of lesions.
 49. The apparatus of claim 40, wherein theprocessor and output device categorize the scanned tissue as eitherhealthy or a vulnerable plaque.
 50. The apparatus of claim 40, whereinthe processor applies a threshold to determine whether the scannedtissue is diseased or not.
 51. The apparatus of claim 40, wherein theprocessor applies a threshold determined by minimizing a classificationbetween two or more groups to establish a boundary calculation thatdetermines whether the scanned tissue is diseased or not.
 52. Theapparatus of claim 40, wherein the output device comprises a screen thatshows basic patient information, the date and time of a scan, adigitized longitudinal view of a scanned tissue, and a digitizedcross-section of a particular section of scanned tissue.
 53. Theapparatus of claim 40, wherein the digitized longitudinal view andcross-sections of the scanned tissue are separated into sections, andwherein each section indicates that the point of tissue represented bythat section is either healthy or diseased.
 54. The apparatus of claim40, wherein the digitized longitudinal view and cross-sections of thescanned tissue are separated into sections, and wherein each sectionindicates one of a continuous grade of a plurality of colorsrepresenting the health of the tissue at that point.
 55. The apparatusof claim 40, wherein the digitized longitudinal view and cross-sectionsof the scanned tissue are separated into sections, and wherein eachsection indicates one of a continuous grade of shades of grayrepresenting the health of the tissue at that point.
 56. The apparatusof claim 40, wherein the processor and output device provide constituentconcentrations of the scanned tissue.
 57. An instrument forcharacterizing portions of tissue in vivo, the instrument comprising a)means for illuminating portions of tissue with near-infrared radiationcomprising any two or more single wavelengths or one or more narrowwavelength bands of near-infrared radiation within a wavelength range ofabout 1100 to 1415 nm; b) means for collecting radiation within thewavelength range that is not absorbed by the tissue; c) means fordetermining from the collected radiation the amounts of absorbance ofradiation by the illuminated tissue; and d) means for discriminating oneilluminated tissue component from another illuminated tissue componentwithin the wavelength range, wherein the discriminating means comprisesi) means for preprocessing the absorbance amounts using a chemometricpreprocessing technique, and ii) means for performing a chemometricdiscrimination algorithm on the preprocessed absorbance amounts tocharacterize the tissues; and e) means for providing an outputindicating the characterization of the illuminated tissue.
 58. A methodof analyzing tissue in blood vessel walls in vivo utilizing a fiberoptic probe, the method comprising: introducing the probe into a bloodvessel; directing onto the tissue in the blood vessel wall near-infraredradiation comprising any two or more single wavelengths or one or morenarrow wavelength bands within a wavelength range of about 1100 to 1415nm; detecting radiation, within a wavelength range from substantially1100 to 1415 nm, which is not absorbed by the blood vessel wall; andanalyzing the detected radiation to categorize the tissue in the bloodvessel wall.
 59. A method of displaying spectral data corresponding to atissue, the method comprising (a) scanning a series of points within thetissue with radiation; (b) detecting radiation reflected from thetissue; (c) processing the detected radiation to generate a set ofnumbers wherein each number in the set characterizes a different pointof scanned tissue; and (d) converting the set of numbers into acontinuous grade output that characterizes the tissue without athreshold.
 60. The method of claim 59, wherein the continuous grading isrepresented by a false color scale.
 61. The method of claim 59, whereinthe continuous grading is represented by a gray scale or differenttones, pitches, or volumes of sound.
 62. The method of claim 59, whereinthe radiation is near-infrared radiation.
 63. The method of claim 59,wherein the tissue is characterized by the constituent concentrationswithin the scanned tissue.